Postdoctoral Appointee – Machine Learning for Extreme Scale Simulations of Wall Bounded Turbulen

Argonne National Laboratory

Lemont, IL

Job posting number: #7071506 (Ref:408988)

Posted: October 28, 2020

Application Deadline: Open Until Filled

Job Description

This is an exciting opportunity for a Postdoctoral Appointee to work on applying Machine Learning (ML) for extreme scale simulations of high Reynolds number turbulent flows on Aurora — the first exa-scale computing platform in the United States. The extreme levels of parallelism available on Aurora create unique optimization challenges for both the CFD solver as well as the ML layer that will be used to learn and develop sub-grid models for turbulence simulations using Hybrid RANS/LES techniques. This research has the potential to revolutionize the development of sub-grid models for high Reynolds number separated flows.

Required Skills

  • Recent PhD in a related field
  • PDE discretization
  • Computational linear algebra 

Skills Valued and Developed Over the Life of the Project (but not required at start)

  • Knowledge of neural networks
  • Experience with data-driven modeling
  • Knowledge of randomized algorithms
  • Knowledge of parallel computing with a preference for (a) MPI, (b) GPU offloading
  • Knowledge of fluid dynamics (a) turbulence (fluid dynamics) theory, (b) turbulence modeling (RANS, LES, DES)
  • Knowledge of unstructured grid methods and finite element methods
  • Visualization (i.e., Paraview) 

Application Documents:

  • Cover letter (optional); uploaded as a PDF document.
  • Resume

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Argonne is an equal opportunity employer, and we value diversity in our workforce. As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne prohibits discrimination or harassment based on an individual's age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.


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